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Authors: Gianluca Torta 1 ; Liliana Ardissono 1 ; Marco Corona 1 ; Luigi La Riccia 2 and Angioletta Voghera 2

Affiliations: 1 Dipartimento di Informatica, Università di Torino, Torino and Italy ; 2 Dipartimento Interateneo di Scienze, Progetto e Politiche del Territorio, Torino and Italy

Keyword(s): Geographic Knowledge, Geographical Constraints, GeoSPARQL, Ecological Networks, Urban Planning.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Business Analytics ; Cardiovascular Technologies ; Collaboration and e-Services ; Computing and Telecommunications in Cardiology ; Data Engineering ; Decision Support Systems ; Decision Support Systems, Remote Data Analysis ; Domain Analysis and Modeling ; e-Business ; Enterprise Information Systems ; Health Engineering and Technology Applications ; Information Systems Analysis and Specification ; Knowledge Engineering and Ontology Development ; Knowledge-Based Systems ; Ontologies and the Semantic Web ; Ontology Engineering ; Semantic Web ; Soft Computing ; Symbolic Systems

Abstract: We describe a framework that supports multiple types of constraint-based reasoning tasks on a geographic domain, by exploiting a semantic representation of the domain itself and of its constraints. Our approach is based on an abstract graph representation of a geographical area and of its relevant properties, for performing the reasoning tasks. As a test-bed, we consider the domain of Ecological Networks (ENs), which describe the structure of existing real ecosystems and help planning their expansion, conservation and improvement by introducing constraints on land use. While some previous work has been done about supporting the verification of compliance of fully specified ENs, we aim at taking a significant step further, by addressing the automatic suggestion of suitable aggregations of land patches into elements of the EN. This automated generation of EN elements is relevant to support the human planner in the design of public policies for land use because it leverages automated to ols to carry out a possibly lengthy and error-prone task. (More)

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Paper citation in several formats:
Torta, G.; Ardissono, L.; Corona, M.; Riccia, L. and Voghera, A. (2018). Ontological Representation of Constraints for Geographical Reasoning. In Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2018) - KEOD; ISBN 978-989-758-330-8; ISSN 2184-3228, SciTePress, pages 136-147. DOI: 10.5220/0006960901360147

@conference{keod18,
author={Gianluca Torta. and Liliana Ardissono. and Marco Corona. and Luigi La Riccia. and Angioletta Voghera.},
title={Ontological Representation of Constraints for Geographical Reasoning},
booktitle={Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2018) - KEOD},
year={2018},
pages={136-147},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006960901360147},
isbn={978-989-758-330-8},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2018) - KEOD
TI - Ontological Representation of Constraints for Geographical Reasoning
SN - 978-989-758-330-8
IS - 2184-3228
AU - Torta, G.
AU - Ardissono, L.
AU - Corona, M.
AU - Riccia, L.
AU - Voghera, A.
PY - 2018
SP - 136
EP - 147
DO - 10.5220/0006960901360147
PB - SciTePress